[unreadable] [unreadable] Successfully treating cancer that has metastasized is considerably more difficult than treating the cancer or precancerous state early in the process of carcinogenesis. Prostate cancer, if caught early, has a 100 percent, five-year survival rate - a surprisingly positive statistic compared to many other types of cancer. For this reason, early detection and localization of prostate cancer through screening is critical. Of late we have been developing computer-aided diagnosis (CAD) tools for detecting malignant and pre-malignant lesions from high resolution Magnetic Resonance (MR) imaging (MRI). Since pre-malignant lesions are widely believed to transform into carcinoma, such a system will help identify and monitor patients with a high risk of prostate cancer and initiate early targeted treatment for regression of the neoplastic process. The broad long term goal of this project is early detection of pre-malignant and malignant prostate lesions, which is extremely significant in (1) Monitoring patients with a high risk of developing prostatic adenocarcinoma, (2) Early targeted treatment for regression of pre-malignant and malignant lesions, and (3) Detection of new histological tissue classes which may be significant in understanding disease processes. The overarching goal of this work is early detection of pre-malignant and malignant lesions and possible identification of new histological tissue classes on high-resolution ex vivo MR imagery via CAD. The proposed work comprises a total of 3 specific aims and 9 tasks. Since it is known that pre-malignant lesions frequently coexist with prostate carcinoma, in this study we propose to only include patients who have been diagnosed with prostate cancer and have been scheduled for a prostatectomy. Under Aim 1 a total of 20 anonymised patient data sets comprising 3 Tesla (T) ex vivo MRI scans with accompanying whole mount histological sections after radical prostatectomy will be obtained. The inclusion of histological data will allow for precise determination of presence and extent of pre-malignant lesions via H&E staining and manual segmentation. Aim 1 will also involve determination of spatial extent of pre-malignant lesions (ground truth) ex vivo by registering the whole mount histological sections with the corresponding ex vivo MRI images. To detect presence and spatial extent of pre-malignant lesions we adopt a two pronged approach using a supervised and unsupervised classification technique. First, under Aim 2 we develop and evaluate a supervised CAD method for detecting pre-cancerous lesions by explicitly modeling textural attributes of HGPIN on ex vivo MRI studies. Under Aim 3 we begin with a supervised CAD model to distinguish cancerous from benign prostate lesions on ex vivo MRI and then apply an unsupervised non-linear dimensionality reduction method to detect new histological tissue classes as those that have characteristics which are intermediate between benign and malignant. Aim 3 will provide (i) a secondary method of detecting pre- cancerous lesions and thus useful in evaluating efficacy of the supervised CAD model developed in Aim 2 and (ii) aid in potential discovery of new histological classes which could facilitate our understanding of cancer progression. The efficacy of the methods proposed under Aims 2, 3 will be evaluated against ground truth derived from histology. This project will be a collaboration between investigators at Rutgers University and the University of Pennsylvania (UPENN). Aim 1 (Data Generation) will be carried out at UPENN while Aims 2 (Tumor ground truth generation) and 3 (CAD model) will be done at Rutgers. [unreadable] [unreadable] [unreadable]